Brain　network　classification　has　attracted　increasing　attention　with　the　widespread　application　in　the　automatic　diagnosis　of　brain　diseases.　However,　limited　by　the　higher　cost　of　detecting　and　marking　for　medical　imaging,　the　amount　of　brain　network　data　is　usually　small,　which　largely　restricts　the　performance　of　current　brain　network　classification　models.　In　this　paper,　we　propose　a　new　sparse　data　augmentation　model　(SDAM)　based　on　EncoderForest　to　effectively　enhance　the　brain　network　data　and　improve　the　classification　performance.　The　EncoderForest　based　SDAM　uses　a　generator　which　innovatively　encodes　the　rules　of　a　set　of　parallel　decision　trees　to　generate　sparse　data　with　only　discriminative　connections.　The　generated　data　expands　the　original　data　set　effectively　by　utilizing　the　advantages　of　EncoderForest　in　learning　data　feature　sparsely　and　constructing　a　feature　association　generation　model　compactly.　In　addition,　the　SDAM　is　flexible　to　combine　with　different　classification　models,　such　as　random　forest,　support　vector　machine,　deep　neural　network,　etc.　The　experimental　results　on　three　common　brain　disease　data　sets　show　that　our　model　is　able　to　reasonably　augment　the　brain　network　data　and　remarkably　improve　the　performance　of　various　classifiers.